Journal ArticleDOI
Application of the Information Criterion to the Estimation of Galaxy Luminosity Function
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In this article, the authors applied Akaike's Information Criterion (AIC) to the determination of the shape of the luminosity function (LF) of galaxies, and derived a formula to obtain the optimal bin number.Abstract:
To determine the exact shape of the luminosity function (LF) of galaxies is one of the central problems in galactic astronomy and observational cosmology. The most popular method to estimate the LF is maximum likelihood, which is clearly understood with the concepts of the information theory. In the field of information theory and statistical inference, great advance has been made by the discovery of Akaike's Information Criterion (AIC). It enables us to perform a direct comparison among different types of model swith different numbers of parameters, and provides us a common basis of the model adequacy. In this paper we applied AIC to the determination of the shape of the LF. We first treated the estimation using stepwise LF(Efstathiou, Ellis and Peterson,1988), and derived a formula to obtain the optimal bin number. In addition, we studied the method to compare the goodness-of-fit of the parametric form (Sandage, Tamman and Yahil, 1979) with stepwise LF.read more
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Dynamics of dark energy
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ISM properties in low-metallicity environments. III. The dust spectral energy distributions of II Zw 40, He 2-10 and NGC 1140
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Imfit: a fast, flexible new program for astronomical image fitting
TL;DR: In this article, the authors describe an open-source astronomical image-fitting program called IMFIT, specialized for galaxies but potentially useful for other sources, which is fast, flexible, and highly extensible.
References
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Journal ArticleDOI
A new look at the statistical model identification
TL;DR: In this article, a new estimate minimum information theoretical criterion estimate (MAICE) is introduced for the purpose of statistical identification, which is free from the ambiguities inherent in the application of conventional hypothesis testing procedure.
Book
Linear statistical inference and its applications
TL;DR: Algebra of Vectors and Matrices, Probability Theory, Tools and Techniques, and Continuous Probability Models.
Journal ArticleDOI
An analytic expression for the luminosity function for galaxies
TL;DR: In this paper, a new analytic approximation for the luminosity function for galaxies is proposed, which shows good agreement with both a luminosity distribution for bright nearby galaxies and a composite luminosity distributions for cluster galaxies.